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Daily blended price ($/1M) — recorded each day, builds into a trend over time.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-04-28 · Source: legacy_model_catalog
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions design...
mistral-small-24b-instruct-2501 is a Text model from Mistral AI (US). HotON.ai tracks it at $0.05 per 1M input tokens and $0.08 per 1M output tokens, with a 33K-token context window. Its composite efficiency score is 89/100 at an estimated $0.000 per successful task.
mistral-small-24b-instruct-2501 is tracked at $0.05 per 1M input tokens and $0.08 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.07 per 1M tokens. Figures are illustrative demo data.
General-purpose text generation, chat, summarization and content workloads where broad capability and low cost matter most.
mistral-small-24b-instruct-2501 supports up to a 33K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, mistral-small-24b-instruct-2501 is cheaper than 91% of models on input price and ranks #188 of 535 by overall efficiency.
Yes — morph-rerank-v3 is a lower-cost option at $0.00 per 1M output tokens, while still covering similar Text use cases. Compare them side by side on HotON.ai.
Ready to paste into articles, papers or AI prompts — prices and date refresh with the live data.
HotON.ai — mistral-small-24b-instruct-2501 (Mistral AI): $0.05/1M input, $0.08/1M output, as of 2026-04-28. https://hoton.ai/en/models/mistralai-mistral-small-24b-instruct-2501Pricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.